With the increasing demand of high video quality and large image size, adaptive interpolation filter (AIF) addresses these issues and conquers the time varying effects resulting in increased coding efficiency, comparing with recent H.264 standard. However, currently most AIF algorithms are based on either frame level or macroblock (MB) level, which are not flexible enough for different video contents in a real codec system, and most of them are facing a severe time consuming problem. This paper proposes a content based coarse to fine AIF algorithm, which can adapt to video contents by adding different filters and conditions from coarse to fine. The overall algorithm has been mainly made up by 3 schemes: frequency analysis based frame level skip interpolation, motion vector modeling based region level interpolation, and edge detection based macroblock level interpolation. According to the experiments, AIF are discovered to be more effective in the high frequency frames, therefore, the condition to skip low frequency frames for generating AIF coefficients has been set. Moreover, by utilizing the motion vector information of previous frames the region level based interpolation has been designed, and Laplacian of Gaussian based macroblock level interpolation has been proposed to drive the interpolation process from coarse to fine. Six 720p and six 1080p video sequences which cover most typical video types have been tested for evaluating the proposed algorithm. The experimental results show that the proposed algorithm reduce total encoding time about 41% for 720p and 25% for 1080p sequences averagely, comparing with Key Technology Areas (KTA) Enhanced AIF algorithm, while obtains a BDPSNR gain up to 0.004 and 3.122 BDBR reduction.

One high profile intra predictor generation engine is proposed in this paper. Firstly, hardware level algorithm optimization for intra 88 (I8MB) mode is introduced. The original candidate pixels for generating prediction samples of I8MB are replaced with boundary pixels of intra 44 (I4MB) blocks. Based on this adoption, full data reuse between predictors of I4MB and filtered samples of I8MB can be achieved with almost no quality loss. Secondly, one lossless two-44-block based parallel predictor generation flow is proposed. The original predictor generation flow is optimized from 16 stages to 10 stages for I4MB and Intra 1616 (I16MB), which saves 37.5% processing cycles. For I8MB, similar methodology with different processing order of 44 scaled blocks is introduced. Thirdly, fully utilized hardwired engines for I4MB, I16MB and I8MB are proposed in this paper. Except DC (direct current) and plane modes, full data reuse among all intra modes of high profile can be achieved. Fourthly, for DC mode, one combined predictor generation process is introduced and predictor generation of I16MB's DC mode is merged into the process of I4MB's DC mode. Moreover, by configuring proposed hardwired engines, predictor generation of I16MB's plane mode and chrominance plane mode can be accomplished with only 50% cycles of original design. Totally, when compared with original full-mode design and latest dynamic mode reused design, the proposed predictor generation engine can achieve 89.5% and 73.2% saving of processing cycles, respectively. Synthesized by TSMC 0.18 µm technology under worst work conditions (1.62 V, 125°C), with 380 MHz and 37.2 k gates, the proposed design can handle real-time high profile intra predictor generation of Super Hi-Vision 4 k4 k@60 fps. The maximum work frequency of our design under worst condition is 468 MHz.

Different encoding modes for variable block size are available in the H.264/AVC standard in order to offer better coding quality. However, this also introduces huge computation time due to the exhaustive check for all modes. In this paper, a fast spatial DIRECT mode decision method for profiles supporting B frame encoding (main profile, high profile, etc.) in H.264/AVC is proposed. Statistical analysis on multiple video sequences is carried out, and the strong relationship of mode selection and rate-distortion (RD) cost between the current DIRECT macroblock (MB) and the co-located MBs is observed. With the check of mode condition, predicted RD cost threshold and dynamic parameter update model, the complex mode decision process can be terminated at an early stage even for small QP cases. Simulation results demonstrate the proposed method can achieve much better performance than the original exhaustive rate-distortion optimization (RDO) based mode decision algorithm by reducing up to 56.8% of encoding time for IBPBP picture group and up to 67.8% of encoding time for IBBPBBP picture group while incurring only negligible bit increment and quality degradation.

Scalable Video Coding (SVC) was standardized as an extension of H.264/AVC with the intention to provide flexible adaptation to heterogeneous networks and different end-user requirements, which provides great scalability in multi-point applications such as video conferencing. However, due to the existence of H.264/AVC-based systems, transcoding between AVC and SVC becomes necessary. Most existing works focus on temporal transcoding, quality transcoding or SVC-to-AVC spatial transcoding while the straightforward re-encoding method requires high computational cost. This paper proposes a low-complexity AVC-to-SVC spatial transcoder based on coarse-level mode mapping for video conferencing scenes. First, to omit unnecessary motion estimations (ME) for layers with reduced resolution, an ME skipping scheme based on AVC mode distribution is proposed with an adaptive search range. Then a probability-profile based scheme is proposed for further mode skipping. After that 3 coarse-level mode-mapping methods are presented for fast mode decision and the adaptive usage of the 3 methods is discussed. Finally, motion vector (MV) refinement is introduced for further lower-layer time reduction. As for the top layer, direct encapsulation is proposed to preserve better quality and another scheme involving inter-layer predictions is also provided for bandwidth-crucial applications. Simulation results show that proposed transcoder achieves up to 92.6% time reduction without significant coding efficiency loss compared to re-encoding method.